Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations9996
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory136.0 B

Variable types

Text4
Categorical6
Numeric7

Alerts

state has constant value "WA" Constant
_2020_census_tract is highly overall correlated with county and 1 other fieldsHigh correlation
cafv_type is highly overall correlated with electric_range and 3 other fieldsHigh correlation
county is highly overall correlated with _2020_census_tract and 3 other fieldsHigh correlation
dol_vehicle_id is highly overall correlated with model_yearHigh correlation
electric_range is highly overall correlated with cafv_type and 2 other fieldsHigh correlation
electric_utility is highly overall correlated with _2020_census_tract and 3 other fieldsHigh correlation
ev_type is highly overall correlated with cafv_type and 2 other fieldsHigh correlation
legislative_district is highly overall correlated with county and 1 other fieldsHigh correlation
make is highly overall correlated with cafv_type and 1 other fieldsHigh correlation
model_year is highly overall correlated with cafv_type and 2 other fieldsHigh correlation
zip_code is highly overall correlated with county and 1 other fieldsHigh correlation
county is highly imbalanced (64.0%) Imbalance
electric_utility is highly imbalanced (54.0%) Imbalance
dol_vehicle_id has unique values Unique
electric_range has 5941 (59.4%) zeros Zeros
base_msrp has 9875 (98.8%) zeros Zeros

Reproduction

Analysis started2025-08-08 01:09:16.066494
Analysis finished2025-08-08 01:09:23.846638
Duration7.78 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct4396
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
2025-08-07T21:09:23.994267image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters99960
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2663 ?
Unique (%)26.6%

Sample

1st row5YJSA1E65N
2nd rowKNDC3DLC5N
3rd row5YJYGDEEXL
4th row3C3CFFGE1G
5th rowKNDCC3LD5K
ValueCountFrequency (%)
7saygdeexp 58
 
0.6%
7saygdee2p 54
 
0.5%
7saygdee9p 53
 
0.5%
7saygdee1p 53
 
0.5%
7saygdee3p 48
 
0.5%
7saygdee7p 47
 
0.5%
7saygdee0p 47
 
0.5%
7saygdee5p 43
 
0.4%
7saygdee6p 43
 
0.4%
7saygdee8p 42
 
0.4%
Other values (4386) 9508
95.1%
2025-08-07T21:09:24.299712image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 8825
 
8.8%
A 6656
 
6.7%
1 6291
 
6.3%
Y 5486
 
5.5%
J 4459
 
4.5%
D 4391
 
4.4%
3 4297
 
4.3%
S 4277
 
4.3%
5 4160
 
4.2%
G 4102
 
4.1%
Other values (23) 47016
47.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 99960
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 8825
 
8.8%
A 6656
 
6.7%
1 6291
 
6.3%
Y 5486
 
5.5%
J 4459
 
4.5%
D 4391
 
4.4%
3 4297
 
4.3%
S 4277
 
4.3%
5 4160
 
4.2%
G 4102
 
4.1%
Other values (23) 47016
47.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 99960
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 8825
 
8.8%
A 6656
 
6.7%
1 6291
 
6.3%
Y 5486
 
5.5%
J 4459
 
4.5%
D 4391
 
4.4%
3 4297
 
4.3%
S 4277
 
4.3%
5 4160
 
4.2%
G 4102
 
4.1%
Other values (23) 47016
47.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 99960
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 8825
 
8.8%
A 6656
 
6.7%
1 6291
 
6.3%
Y 5486
 
5.5%
J 4459
 
4.5%
D 4391
 
4.4%
3 4297
 
4.3%
S 4277
 
4.3%
5 4160
 
4.2%
G 4102
 
4.1%
Other values (23) 47016
47.0%

county
Categorical

High correlation  Imbalance 

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
King
7219 
Clark
1025 
Kitsap
 
581
Snohomish
 
424
Thurston
 
308
Other values (16)
 
439

Length

Max length11
Median length4
Mean length4.6854742
Min length4

Characters and Unicode

Total characters46836
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowYakima
2nd rowYakima
3rd rowSnohomish
4th rowYakima
5th rowKitsap

Common Values

ValueCountFrequency (%)
King 7219
72.2%
Clark 1025
 
10.3%
Kitsap 581
 
5.8%
Snohomish 424
 
4.2%
Thurston 308
 
3.1%
Yakima 127
 
1.3%
Cowlitz 107
 
1.1%
Jefferson 85
 
0.9%
Island 54
 
0.5%
Whitman 12
 
0.1%
Other values (11) 54
 
0.5%

Length

2025-08-07T21:09:24.443251image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
king 7219
72.2%
clark 1025
 
10.2%
kitsap 581
 
5.8%
snohomish 424
 
4.2%
thurston 308
 
3.1%
yakima 127
 
1.3%
cowlitz 107
 
1.1%
jefferson 85
 
0.8%
island 54
 
0.5%
stevens 12
 
0.1%
Other values (11) 59
 
0.6%

Most occurring characters

ValueCountFrequency (%)
i 8491
18.1%
n 8128
17.4%
K 7806
16.7%
g 7228
15.4%
a 1991
 
4.3%
s 1469
 
3.1%
r 1421
 
3.0%
o 1352
 
2.9%
l 1244
 
2.7%
h 1175
 
2.5%
Other values (23) 6531
13.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46836
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 8491
18.1%
n 8128
17.4%
K 7806
16.7%
g 7228
15.4%
a 1991
 
4.3%
s 1469
 
3.1%
r 1421
 
3.0%
o 1352
 
2.9%
l 1244
 
2.7%
h 1175
 
2.5%
Other values (23) 6531
13.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46836
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 8491
18.1%
n 8128
17.4%
K 7806
16.7%
g 7228
15.4%
a 1991
 
4.3%
s 1469
 
3.1%
r 1421
 
3.0%
o 1352
 
2.9%
l 1244
 
2.7%
h 1175
 
2.5%
Other values (23) 6531
13.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46836
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 8491
18.1%
n 8128
17.4%
K 7806
16.7%
g 7228
15.4%
a 1991
 
4.3%
s 1469
 
3.1%
r 1421
 
3.0%
o 1352
 
2.9%
l 1244
 
2.7%
h 1175
 
2.5%
Other values (23) 6531
13.9%

city
Text

Distinct131
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
2025-08-07T21:09:24.652994image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length17
Median length16
Mean length7.8923569
Min length3

Characters and Unicode

Total characters78892
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)0.3%

Sample

1st rowGranger
2nd rowYakima
3rd rowEverett
4th rowYakima
5th rowBremerton
ValueCountFrequency (%)
seattle 2138
19.8%
kirkland 1415
13.1%
bellevue 1153
 
10.7%
vancouver 812
 
7.5%
redmond 442
 
4.1%
kent 413
 
3.8%
renton 286
 
2.6%
island 236
 
2.2%
bothell 230
 
2.1%
bainbridge 227
 
2.1%
Other values (144) 3473
32.1%
2025-08-07T21:09:25.015681image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 12493
15.8%
l 8078
 
10.2%
a 7001
 
8.9%
t 6123
 
7.8%
n 5581
 
7.1%
r 4057
 
5.1%
d 3446
 
4.4%
i 3383
 
4.3%
o 3163
 
4.0%
u 2977
 
3.8%
Other values (39) 22590
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 78892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 12493
15.8%
l 8078
 
10.2%
a 7001
 
8.9%
t 6123
 
7.8%
n 5581
 
7.1%
r 4057
 
5.1%
d 3446
 
4.4%
i 3383
 
4.3%
o 3163
 
4.0%
u 2977
 
3.8%
Other values (39) 22590
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 78892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 12493
15.8%
l 8078
 
10.2%
a 7001
 
8.9%
t 6123
 
7.8%
n 5581
 
7.1%
r 4057
 
5.1%
d 3446
 
4.4%
i 3383
 
4.3%
o 3163
 
4.0%
u 2977
 
3.8%
Other values (39) 22590
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 78892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 12493
15.8%
l 8078
 
10.2%
a 7001
 
8.9%
t 6123
 
7.8%
n 5581
 
7.1%
r 4057
 
5.1%
d 3446
 
4.4%
i 3383
 
4.3%
o 3163
 
4.0%
u 2977
 
3.8%
Other values (39) 22590
28.6%

state
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
WA
9996 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters19992
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWA
2nd rowWA
3rd rowWA
4th rowWA
5th rowWA

Common Values

ValueCountFrequency (%)
WA 9996
100.0%

Length

2025-08-07T21:09:25.145488image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-07T21:09:25.225205image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
wa 9996
100.0%

Most occurring characters

ValueCountFrequency (%)
W 9996
50.0%
A 9996
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19992
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 9996
50.0%
A 9996
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19992
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 9996
50.0%
A 9996
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19992
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 9996
50.0%
A 9996
50.0%

zip_code
Real number (ℝ)

High correlation 

Distinct196
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98184.26
Minimum98001
Maximum99362
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-08-07T21:09:25.327903image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum98001
5-th percentile98004
Q198033
median98103
Q398178
95-th percentile98684.25
Maximum99362
Range1361
Interquartile range (IQR)145

Descriptive statistics

Standard deviation233.01822
Coefficient of variation (CV)0.0023732747
Kurtosis1.761378
Mean98184.26
Median Absolute Deviation (MAD)70
Skewness1.6437925
Sum9.8144986 × 108
Variance54297.491
MonotonicityNot monotonic
2025-08-07T21:09:25.474962image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98033 709
 
7.1%
98034 706
 
7.1%
98125 562
 
5.6%
98004 501
 
5.0%
98052 440
 
4.4%
98006 262
 
2.6%
98177 260
 
2.6%
98005 257
 
2.6%
98110 227
 
2.3%
98685 225
 
2.3%
Other values (186) 5847
58.5%
ValueCountFrequency (%)
98001 19
 
0.2%
98002 57
 
0.6%
98003 24
 
0.2%
98004 501
5.0%
98005 257
2.6%
98006 262
2.6%
98007 88
 
0.9%
98008 68
 
0.7%
98011 165
 
1.7%
98012 55
 
0.6%
ValueCountFrequency (%)
99362 5
0.1%
99349 1
 
< 0.1%
99223 1
 
< 0.1%
99203 1
 
< 0.1%
99181 1
 
< 0.1%
99163 11
0.1%
99161 1
 
< 0.1%
99114 1
 
< 0.1%
99026 10
0.1%
99013 1
 
< 0.1%

model_year
Real number (ℝ)

High correlation 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2021.503
Minimum2008
Maximum2026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-08-07T21:09:25.594800image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2015
Q12020
median2023
Q32024
95-th percentile2025
Maximum2026
Range18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0597667
Coefficient of variation (CV)0.0015136098
Kurtosis0.72693454
Mean2021.503
Median Absolute Deviation (MAD)1
Skewness-1.1552955
Sum20206944
Variance9.3621721
MonotonicityNot monotonic
2025-08-07T21:09:25.699307image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2023 2309
23.1%
2024 1903
19.0%
2022 1141
11.4%
2025 879
 
8.8%
2021 862
 
8.6%
2018 627
 
6.3%
2020 550
 
5.5%
2019 456
 
4.6%
2017 317
 
3.2%
2016 255
 
2.6%
Other values (8) 697
 
7.0%
ValueCountFrequency (%)
2008 2
 
< 0.1%
2010 1
 
< 0.1%
2011 19
 
0.2%
2012 57
 
0.6%
2013 167
 
1.7%
2014 157
 
1.6%
2015 215
 
2.2%
2016 255
2.6%
2017 317
3.2%
2018 627
6.3%
ValueCountFrequency (%)
2026 79
 
0.8%
2025 879
 
8.8%
2024 1903
19.0%
2023 2309
23.1%
2022 1141
11.4%
2021 862
 
8.6%
2020 550
 
5.5%
2019 456
 
4.6%
2018 627
 
6.3%
2017 317
 
3.2%

make
Categorical

High correlation 

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
TESLA
4323 
CHEVROLET
690 
NISSAN
688 
BMW
489 
KIA
476 
Other values (33)
3330 

Length

Max length13
Median length10
Mean length5.5453181
Min length3

Characters and Unicode

Total characters55431
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowTESLA
2nd rowKIA
3rd rowTESLA
4th rowFIAT
5th rowKIA

Common Values

ValueCountFrequency (%)
TESLA 4323
43.2%
CHEVROLET 690
 
6.9%
NISSAN 688
 
6.9%
BMW 489
 
4.9%
KIA 476
 
4.8%
FORD 441
 
4.4%
TOYOTA 434
 
4.3%
HYUNDAI 357
 
3.6%
VOLVO 284
 
2.8%
RIVIAN 261
 
2.6%
Other values (28) 1553
 
15.5%

Length

2025-08-07T21:09:25.821783image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tesla 4323
43.2%
chevrolet 690
 
6.9%
nissan 688
 
6.9%
bmw 489
 
4.9%
kia 476
 
4.8%
ford 441
 
4.4%
toyota 434
 
4.3%
hyundai 357
 
3.6%
volvo 284
 
2.8%
rivian 261
 
2.6%
Other values (30) 1573
 
15.7%

Most occurring characters

ValueCountFrequency (%)
A 7514
13.6%
E 7185
13.0%
S 6543
11.8%
T 6018
10.9%
L 5908
10.7%
O 3075
 
5.5%
I 2586
 
4.7%
N 2559
 
4.6%
R 2020
 
3.6%
V 1776
 
3.2%
Other values (18) 10247
18.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55431
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 7514
13.6%
E 7185
13.0%
S 6543
11.8%
T 6018
10.9%
L 5908
10.7%
O 3075
 
5.5%
I 2586
 
4.7%
N 2559
 
4.6%
R 2020
 
3.6%
V 1776
 
3.2%
Other values (18) 10247
18.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55431
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 7514
13.6%
E 7185
13.0%
S 6543
11.8%
T 6018
10.9%
L 5908
10.7%
O 3075
 
5.5%
I 2586
 
4.7%
N 2559
 
4.6%
R 2020
 
3.6%
V 1776
 
3.2%
Other values (18) 10247
18.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55431
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 7514
13.6%
E 7185
13.0%
S 6543
11.8%
T 6018
10.9%
L 5908
10.7%
O 3075
 
5.5%
I 2586
 
4.7%
N 2559
 
4.6%
R 2020
 
3.6%
V 1776
 
3.2%
Other values (18) 10247
18.5%

model
Text

Distinct142
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
2025-08-07T21:09:26.023744image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length21
Median length7
Mean length6.5136054
Min length2

Characters and Unicode

Total characters65110
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)0.2%

Sample

1st rowMODEL S
2nd rowEV6
3rd rowMODEL Y
4th row500
5th rowNIRO
ValueCountFrequency (%)
model 4278
26.7%
y 2154
 
13.5%
3 1505
 
9.4%
leaf 600
 
3.8%
bolt 413
 
2.6%
prime 321
 
2.0%
phev 321
 
2.0%
s 316
 
2.0%
x 305
 
1.9%
ev 301
 
1.9%
Other values (141) 5480
34.3%
2025-08-07T21:09:26.391250image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 7541
 
11.6%
O 6131
 
9.4%
L 6105
 
9.4%
5998
 
9.2%
M 5078
 
7.8%
D 4629
 
7.1%
A 2643
 
4.1%
Y 2452
 
3.8%
R 2403
 
3.7%
I 2247
 
3.5%
Other values (30) 19883
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65110
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 7541
 
11.6%
O 6131
 
9.4%
L 6105
 
9.4%
5998
 
9.2%
M 5078
 
7.8%
D 4629
 
7.1%
A 2643
 
4.1%
Y 2452
 
3.8%
R 2403
 
3.7%
I 2247
 
3.5%
Other values (30) 19883
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65110
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 7541
 
11.6%
O 6131
 
9.4%
L 6105
 
9.4%
5998
 
9.2%
M 5078
 
7.8%
D 4629
 
7.1%
A 2643
 
4.1%
Y 2452
 
3.8%
R 2403
 
3.7%
I 2247
 
3.5%
Other values (30) 19883
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65110
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 7541
 
11.6%
O 6131
 
9.4%
L 6105
 
9.4%
5998
 
9.2%
M 5078
 
7.8%
D 4629
 
7.1%
A 2643
 
4.1%
Y 2452
 
3.8%
R 2403
 
3.7%
I 2247
 
3.5%
Other values (30) 19883
30.5%

ev_type
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
Battery Electric Vehicle (BEV)
8025 
Plug-in Hybrid Electric Vehicle (PHEV)
1971 

Length

Max length38
Median length30
Mean length31.577431
Min length30

Characters and Unicode

Total characters315648
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBattery Electric Vehicle (BEV)
2nd rowBattery Electric Vehicle (BEV)
3rd rowBattery Electric Vehicle (BEV)
4th rowBattery Electric Vehicle (BEV)
5th rowPlug-in Hybrid Electric Vehicle (PHEV)

Common Values

ValueCountFrequency (%)
Battery Electric Vehicle (BEV) 8025
80.3%
Plug-in Hybrid Electric Vehicle (PHEV) 1971
 
19.7%

Length

2025-08-07T21:09:26.527149image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-07T21:09:26.626627image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
electric 9996
23.8%
vehicle 9996
23.8%
battery 8025
19.1%
bev 8025
19.1%
plug-in 1971
 
4.7%
hybrid 1971
 
4.7%
phev 1971
 
4.7%

Most occurring characters

ValueCountFrequency (%)
e 38013
12.0%
31959
10.1%
c 29988
9.5%
t 26046
 
8.3%
i 23934
 
7.6%
l 21963
 
7.0%
V 19992
 
6.3%
r 19992
 
6.3%
E 19992
 
6.3%
B 16050
 
5.1%
Other values (13) 67719
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 315648
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 38013
12.0%
31959
10.1%
c 29988
9.5%
t 26046
 
8.3%
i 23934
 
7.6%
l 21963
 
7.0%
V 19992
 
6.3%
r 19992
 
6.3%
E 19992
 
6.3%
B 16050
 
5.1%
Other values (13) 67719
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 315648
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 38013
12.0%
31959
10.1%
c 29988
9.5%
t 26046
 
8.3%
i 23934
 
7.6%
l 21963
 
7.0%
V 19992
 
6.3%
r 19992
 
6.3%
E 19992
 
6.3%
B 16050
 
5.1%
Other values (13) 67719
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 315648
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 38013
12.0%
31959
10.1%
c 29988
9.5%
t 26046
 
8.3%
i 23934
 
7.6%
l 21963
 
7.0%
V 19992
 
6.3%
r 19992
 
6.3%
E 19992
 
6.3%
B 16050
 
5.1%
Other values (13) 67719
21.5%

cafv_type
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
Eligibility unknown as battery range has not been researched
5941 
Clean Alternative Fuel Vehicle Eligible
3163 
Not eligible due to low battery range
892 

Length

Max length60
Median length60
Mean length51.302621
Min length37

Characters and Unicode

Total characters512821
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEligibility unknown as battery range has not been researched
2nd rowEligibility unknown as battery range has not been researched
3rd rowClean Alternative Fuel Vehicle Eligible
4th rowClean Alternative Fuel Vehicle Eligible
5th rowNot eligible due to low battery range

Common Values

ValueCountFrequency (%)
Eligibility unknown as battery range has not been researched 5941
59.4%
Clean Alternative Fuel Vehicle Eligible 3163
31.6%
Not eligible due to low battery range 892
 
8.9%

Length

2025-08-07T21:09:26.723940image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-07T21:09:26.821602image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
not 6833
9.0%
battery 6833
9.0%
range 6833
9.0%
eligibility 5941
 
7.9%
unknown 5941
 
7.9%
been 5941
 
7.9%
researched 5941
 
7.9%
has 5941
 
7.9%
as 5941
 
7.9%
eligible 4055
 
5.4%
Other values (7) 15328
20.3%

Most occurring characters

ValueCountFrequency (%)
e 68188
13.3%
65532
12.8%
n 42864
 
8.4%
i 38200
 
7.4%
a 37815
 
7.4%
t 33658
 
6.6%
l 33536
 
6.5%
r 28711
 
5.6%
b 22770
 
4.4%
s 17823
 
3.5%
Other values (16) 123724
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 512821
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 68188
13.3%
65532
12.8%
n 42864
 
8.4%
i 38200
 
7.4%
a 37815
 
7.4%
t 33658
 
6.6%
l 33536
 
6.5%
r 28711
 
5.6%
b 22770
 
4.4%
s 17823
 
3.5%
Other values (16) 123724
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 512821
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 68188
13.3%
65532
12.8%
n 42864
 
8.4%
i 38200
 
7.4%
a 37815
 
7.4%
t 33658
 
6.6%
l 33536
 
6.5%
r 28711
 
5.6%
b 22770
 
4.4%
s 17823
 
3.5%
Other values (16) 123724
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 512821
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 68188
13.3%
65532
12.8%
n 42864
 
8.4%
i 38200
 
7.4%
a 37815
 
7.4%
t 33658
 
6.6%
l 33536
 
6.5%
r 28711
 
5.6%
b 22770
 
4.4%
s 17823
 
3.5%
Other values (16) 123724
24.1%

electric_range
Real number (ℝ)

High correlation  Zeros 

Distinct101
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.160464
Minimum0
Maximum337
Zeros5941
Zeros (%)59.4%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-08-07T21:09:26.934754image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q340
95-th percentile239
Maximum337
Range337
Interquartile range (IQR)40

Descriptive statistics

Standard deviation84.665505
Coefficient of variation (CV)1.7952645
Kurtosis1.8548695
Mean47.160464
Median Absolute Deviation (MAD)0
Skewness1.8072987
Sum471416
Variance7168.2478
MonotonicityNot monotonic
2025-08-07T21:09:27.074828image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5941
59.4%
215 305
 
3.1%
84 201
 
2.0%
25 173
 
1.7%
238 172
 
1.7%
42 172
 
1.7%
220 158
 
1.6%
32 145
 
1.5%
21 139
 
1.4%
19 103
 
1.0%
Other values (91) 2487
24.9%
ValueCountFrequency (%)
0 5941
59.4%
1 1
 
< 0.1%
6 48
 
0.5%
8 3
 
< 0.1%
10 5
 
0.1%
12 6
 
0.1%
13 21
 
0.2%
14 33
 
0.3%
15 3
 
< 0.1%
16 27
 
0.3%
ValueCountFrequency (%)
337 1
 
< 0.1%
330 17
 
0.2%
322 81
0.8%
308 14
 
0.1%
293 24
 
0.2%
291 81
0.8%
289 30
 
0.3%
288 2
 
< 0.1%
270 12
 
0.1%
266 74
0.7%

base_msrp
Real number (ℝ)

Zeros 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean715.2586
Minimum0
Maximum184400
Zeros9875
Zeros (%)98.8%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-08-07T21:09:27.192892image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum184400
Range184400
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6819.8221
Coefficient of variation (CV)9.5347642
Kurtosis139.53512
Mean715.2586
Median Absolute Deviation (MAD)0
Skewness10.800979
Sum7149725
Variance46509974
MonotonicityNot monotonic
2025-08-07T21:09:27.303551image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 9875
98.8%
69900 52
 
0.5%
31950 13
 
0.1%
52900 10
 
0.1%
64950 7
 
0.1%
36900 4
 
< 0.1%
54950 4
 
< 0.1%
44100 4
 
< 0.1%
34995 3
 
< 0.1%
45600 3
 
< 0.1%
Other values (12) 21
 
0.2%
ValueCountFrequency (%)
0 9875
98.8%
31950 13
 
0.1%
32250 2
 
< 0.1%
33950 1
 
< 0.1%
34995 3
 
< 0.1%
36800 2
 
< 0.1%
36900 4
 
< 0.1%
39995 2
 
< 0.1%
44100 4
 
< 0.1%
45600 3
 
< 0.1%
ValueCountFrequency (%)
184400 1
 
< 0.1%
110950 1
 
< 0.1%
98950 2
 
< 0.1%
81100 1
 
< 0.1%
69900 52
0.5%
64950 7
 
0.1%
59900 3
 
< 0.1%
55700 2
 
< 0.1%
54950 4
 
< 0.1%
53400 3
 
< 0.1%

legislative_district
Real number (ℝ)

High correlation 

Distinct42
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.237395
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-08-07T21:09:27.431041image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q123
median41
Q346
95-th percentile48
Maximum49
Range48
Interquartile range (IQR)23

Descriptive statistics

Standard deviation14.151434
Coefficient of variation (CV)0.41333269
Kurtosis-0.40016752
Mean34.237395
Median Absolute Deviation (MAD)7
Skewness-0.90622466
Sum342237
Variance200.2631
MonotonicityNot monotonic
2025-08-07T21:09:27.565259image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
45 1287
 
12.9%
48 1076
 
10.8%
41 894
 
8.9%
46 709
 
7.1%
47 509
 
5.1%
43 472
 
4.7%
1 451
 
4.5%
23 423
 
4.2%
17 416
 
4.2%
18 379
 
3.8%
Other values (32) 3380
33.8%
ValueCountFrequency (%)
1 451
4.5%
2 69
 
0.7%
3 1
 
< 0.1%
5 117
 
1.2%
6 1
 
< 0.1%
7 12
 
0.1%
9 12
 
0.1%
10 62
 
0.6%
11 302
3.0%
12 8
 
0.1%
ValueCountFrequency (%)
49 218
 
2.2%
48 1076
10.8%
47 509
 
5.1%
46 709
7.1%
45 1287
12.9%
44 148
 
1.5%
43 472
 
4.7%
41 894
8.9%
40 5
 
0.1%
39 55
 
0.6%

dol_vehicle_id
Real number (ℝ)

High correlation  Unique 

Distinct9996
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3716222 × 108
Minimum110057
Maximum4.7861185 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-08-07T21:09:27.691668image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum110057
5-th percentile1.1809841 × 108
Q12.0706428 × 108
median2.5548821 × 108
Q32.7280564 × 108
95-th percentile2.8963993 × 108
Maximum4.7861185 × 108
Range4.7850179 × 108
Interquartile range (IQR)65741359

Descriptive statistics

Standard deviation69513436
Coefficient of variation (CV)0.29310502
Kurtosis3.6719682
Mean2.3716222 × 108
Median Absolute Deviation (MAD)22751740
Skewness-0.11830138
Sum2.3706735 × 1012
Variance4.8321178 × 1015
MonotonicityNot monotonic
2025-08-07T21:09:27.835554image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
187279214 1
 
< 0.1%
239664645 1
 
< 0.1%
275495265 1
 
< 0.1%
272873387 1
 
< 0.1%
272075401 1
 
< 0.1%
258794434 1
 
< 0.1%
180607479 1
 
< 0.1%
251223143 1
 
< 0.1%
6113390 1
 
< 0.1%
257937753 1
 
< 0.1%
Other values (9986) 9986
99.9%
ValueCountFrequency (%)
110057 1
< 0.1%
204795 1
< 0.1%
397141 1
< 0.1%
1465033 1
< 0.1%
1495133 1
< 0.1%
1562389 1
< 0.1%
1663686 1
< 0.1%
1697706 1
< 0.1%
1720295 1
< 0.1%
1730781 1
< 0.1%
ValueCountFrequency (%)
478611850 1
< 0.1%
478553316 1
< 0.1%
478493809 1
< 0.1%
478465123 1
< 0.1%
478437018 1
< 0.1%
478429360 1
< 0.1%
478424999 1
< 0.1%
478417159 1
< 0.1%
478337415 1
< 0.1%
478296605 1
< 0.1%
Distinct196
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
2025-08-07T21:09:28.057856image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length56
Median length56
Mean length55.660464
Min length54

Characters and Unicode

Total characters556382
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)0.3%

Sample

1st row{'type': 'Point', 'coordinates': [-120.1871, 46.33949]}
2nd row{'type': 'Point', 'coordinates': [-120.52041, 46.59751]}
3rd row{'type': 'Point', 'coordinates': [-122.18637, 47.89251]}
4th row{'type': 'Point', 'coordinates': [-120.60199, 46.59817]}
5th row{'type': 'Point', 'coordinates': [-122.65223, 47.57192]}
ValueCountFrequency (%)
type 9996
20.0%
coordinates 9996
20.0%
point 9996
20.0%
122.2066 709
 
1.4%
47.67887 709
 
1.4%
122.22901 706
 
1.4%
47.72201 706
 
1.4%
122.30253 562
 
1.1%
47.72656 562
 
1.1%
122.1872 501
 
1.0%
Other values (385) 15537
31.1%
2025-08-07T21:09:28.419355image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 59976
 
10.8%
39984
 
7.2%
2 31124
 
5.6%
o 29988
 
5.4%
t 29988
 
5.4%
1 22142
 
4.0%
: 19992
 
3.6%
e 19992
 
3.6%
i 19992
 
3.6%
n 19992
 
3.6%
Other values (23) 263212
47.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 556382
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 59976
 
10.8%
39984
 
7.2%
2 31124
 
5.6%
o 29988
 
5.4%
t 29988
 
5.4%
1 22142
 
4.0%
: 19992
 
3.6%
e 19992
 
3.6%
i 19992
 
3.6%
n 19992
 
3.6%
Other values (23) 263212
47.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 556382
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 59976
 
10.8%
39984
 
7.2%
2 31124
 
5.6%
o 29988
 
5.4%
t 29988
 
5.4%
1 22142
 
4.0%
: 19992
 
3.6%
e 19992
 
3.6%
i 19992
 
3.6%
n 19992
 
3.6%
Other values (23) 263212
47.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 556382
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 59976
 
10.8%
39984
 
7.2%
2 31124
 
5.6%
o 29988
 
5.4%
t 29988
 
5.4%
1 22142
 
4.0%
: 19992
 
3.6%
e 19992
 
3.6%
i 19992
 
3.6%
n 19992
 
3.6%
Other values (23) 263212
47.3%

electric_utility
Categorical

High correlation  Imbalance 

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)
4803 
CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)
2415 
PUGET SOUND ENERGY INC
1363 
BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF CLARK COUNTY - (WA)
1019 
PACIFICORP
 
124
Other values (16)
 
272

Length

Max length86
Median length85
Mean length43.593337
Min length10

Characters and Unicode

Total characters435759
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowPACIFICORP
2nd rowPACIFICORP
3rd rowPUGET SOUND ENERGY INC
4th rowPACIFICORP
5th rowPUGET SOUND ENERGY INC

Common Values

ValueCountFrequency (%)
PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA) 4803
48.0%
CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA) 2415
24.2%
PUGET SOUND ENERGY INC 1363
 
13.6%
BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF CLARK COUNTY - (WA) 1019
 
10.2%
PACIFICORP 124
 
1.2%
BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF COWLITZ COUNTY 107
 
1.1%
BONNEVILLE POWER ADMINISTRATION||PUGET SOUND ENERGY INC||PUD NO 1 OF JEFFERSON COUNTY 81
 
0.8%
AVISTA CORP 15
 
0.2%
BONNEVILLE POWER ADMINISTRATION||PUD 1 OF SNOHOMISH COUNTY 14
 
0.1%
NO KNOWN ELECTRIC UTILITY SERVICE 13
 
0.1%
Other values (11) 42
 
0.4%

Length

2025-08-07T21:09:28.571027image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of 10890
13.9%
10663
13.6%
wa 8248
10.5%
tacoma 7219
9.2%
energy 6247
8.0%
sound 6247
8.0%
puget 6166
7.9%
inc||city 4803
 
6.1%
city 2416
 
3.1%
seattle 2415
 
3.1%
Other values (40) 12870
16.5%

Most occurring characters

ValueCountFrequency (%)
68188
15.6%
T 31876
 
7.3%
A 31256
 
7.2%
O 31013
 
7.1%
E 27587
 
6.3%
N 26411
 
6.1%
C 25853
 
5.9%
I 21359
 
4.9%
Y 17152
 
3.9%
U 15020
 
3.4%
Other values (21) 140044
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 435759
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
68188
15.6%
T 31876
 
7.3%
A 31256
 
7.2%
O 31013
 
7.1%
E 27587
 
6.3%
N 26411
 
6.1%
C 25853
 
5.9%
I 21359
 
4.9%
Y 17152
 
3.9%
U 15020
 
3.4%
Other values (21) 140044
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 435759
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
68188
15.6%
T 31876
 
7.3%
A 31256
 
7.2%
O 31013
 
7.1%
E 27587
 
6.3%
N 26411
 
6.1%
C 25853
 
5.9%
I 21359
 
4.9%
Y 17152
 
3.9%
U 15020
 
3.4%
Other values (21) 140044
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 435759
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
68188
15.6%
T 31876
 
7.3%
A 31256
 
7.2%
O 31013
 
7.1%
E 27587
 
6.3%
N 26411
 
6.1%
C 25853
 
5.9%
I 21359
 
4.9%
Y 17152
 
3.9%
U 15020
 
3.4%
Other values (21) 140044
32.1%

_2020_census_tract
Real number (ℝ)

High correlation 

Distinct743
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3033562 × 1010
Minimum5.300796 × 1010
Maximum5.307794 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-08-07T21:09:28.696682image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum5.300796 × 1010
5-th percentile5.3011041 × 1010
Q15.3033005 × 1010
median5.3033023 × 1010
Q35.303303 × 1010
95-th percentile5.3061053 × 1010
Maximum5.307794 × 1010
Range69979807
Interquartile range (IQR)24701

Descriptive statistics

Standard deviation12350003
Coefficient of variation (CV)0.00023287146
Kurtosis3.1507729
Mean5.3033562 × 1010
Median Absolute Deviation (MAD)9721
Skewness1.0552974
Sum5.3012348 × 1014
Variance1.5252257 × 1014
MonotonicityNot monotonic
2025-08-07T21:09:28.841291image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.303303232 × 1010158
 
1.6%
5.303303232 × 1010154
 
1.5%
5.30330226 × 1010136
 
1.4%
5.3033024 × 1010124
 
1.2%
5.30330225 × 1010121
 
1.2%
5.30330219 × 1010115
 
1.2%
5.30330222 × 1010108
 
1.1%
5.30330219 × 1010103
 
1.0%
5.30330005 × 1010102
 
1.0%
5.30350907 × 101090
 
0.9%
Other values (733) 8785
87.9%
ValueCountFrequency (%)
5.30079602 × 10102
< 0.1%
5.30079602 × 10102
< 0.1%
5.30079607 × 10101
< 0.1%
5.30079608 × 10102
< 0.1%
5.30090008 × 10101
< 0.1%
5.30090009 × 10101
< 0.1%
5.30090012 × 10102
< 0.1%
5.30090014 × 10102
< 0.1%
5.30090019 × 10101
< 0.1%
5.3009002 × 10101
< 0.1%
ValueCountFrequency (%)
5.307794001 × 10102
 
< 0.1%
5.307794001 × 10103
 
< 0.1%
5.307794 × 10103
 
< 0.1%
5.307794 × 10101
 
< 0.1%
5.30770034 × 10102
 
< 0.1%
5.30770032 × 101010
0.1%
5.3077003 × 10101
 
< 0.1%
5.3077003 × 10101
 
< 0.1%
5.30770029 × 10103
 
< 0.1%
5.30770028 × 10109
0.1%

Interactions

2025-08-07T21:09:22.755714image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:18.616239image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:19.330973image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:20.015716image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:20.719042image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:21.411453image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:22.085142image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:22.866100image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:18.717947image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:19.431219image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:20.115367image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:20.818927image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:21.504550image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:22.175817image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:22.962538image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:18.828654image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:19.527458image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:20.215505image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:20.913448image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:21.609090image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:22.271062image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:23.063817image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:18.926697image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:19.629018image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:20.314651image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:21.009451image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:21.705769image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:22.371446image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:23.166930image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:19.043310image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:19.729924image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:20.424942image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:21.114481image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:21.807316image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:22.464157image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:23.258521image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:19.133215image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:19.821771image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:20.511542image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:21.206842image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:21.891152image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:22.559244image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:23.354769image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:19.232521image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:19.912616image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:20.612622image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:21.308134image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:21.985574image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-08-07T21:09:22.657536image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2025-08-07T21:09:28.950553image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
_2020_census_tractbase_msrpcafv_typecountydol_vehicle_idelectric_rangeelectric_utilityev_typelegislative_districtmakemodel_yearzip_code
_2020_census_tract1.0000.0020.0350.999-0.013-0.0190.7820.045-0.0400.0460.027-0.289
base_msrp0.0021.0000.1450.000-0.0520.1170.0000.114-0.0070.101-0.1600.007
cafv_type0.0350.1451.0000.0760.3510.6540.0840.7420.0630.5520.5490.064
county0.9990.0000.0761.0000.0350.0520.7900.0790.5660.0350.0950.824
dol_vehicle_id-0.013-0.0520.3510.0351.000-0.1920.0340.0830.0250.1100.512-0.022
electric_range-0.0190.1170.6540.052-0.1921.0000.0530.569-0.0480.348-0.6310.082
electric_utility0.7820.0000.0840.7900.0340.0531.0000.0920.5090.0480.0940.703
ev_type0.0450.1140.7420.0790.0830.5690.0921.0000.0720.7270.1720.065
legislative_district-0.040-0.0070.0630.5660.025-0.0480.5090.0721.0000.0680.049-0.412
make0.0460.1010.5520.0350.1100.3480.0480.7270.0681.0000.2150.063
model_year0.027-0.1600.5490.0950.512-0.6310.0940.1720.0490.2151.000-0.085
zip_code-0.2890.0070.0640.824-0.0220.0820.7030.065-0.4120.063-0.0851.000

Missing values

2025-08-07T21:09:23.518257image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-07T21:09:23.744167image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

vin_1_10countycitystatezip_codemodel_yearmakemodelev_typecafv_typeelectric_rangebase_msrplegislative_districtdol_vehicle_idgeocoded_columnelectric_utility_2020_census_tract
05YJSA1E65NYakimaGrangerWA98932.02022TESLAMODEL SBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0015.0187279214{'type': 'Point', 'coordinates': [-120.1871, 46.33949]}PACIFICORP5.307700e+10
1KNDC3DLC5NYakimaYakimaWA98902.02022KIAEV6Battery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0015.0210098241{'type': 'Point', 'coordinates': [-120.52041, 46.59751]}PACIFICORP5.307700e+10
25YJYGDEEXLSnohomishEverettWA98208.02020TESLAMODEL YBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible291044.0121781950{'type': 'Point', 'coordinates': [-122.18637, 47.89251]}PUGET SOUND ENERGY INC5.306104e+10
33C3CFFGE1GYakimaYakimaWA98908.02016FIAT500Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible84014.0180778377{'type': 'Point', 'coordinates': [-120.60199, 46.59817]}PACIFICORP5.307700e+10
4KNDCC3LD5KKitsapBremertonWA98312.02019KIANIROPlug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range26026.02581225{'type': 'Point', 'coordinates': [-122.65223, 47.57192]}PUGET SOUND ENERGY INC5.303508e+10
55YJXCAE29LKitsapSilverdaleWA98383.02020TESLAMODEL XBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible293023.01843054{'type': 'Point', 'coordinates': [-122.69275, 47.65171]}PUGET SOUND ENERGY INC5.303509e+10
65YJ3E1EB6LKingKentWA98030.02020TESLAMODEL 3Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible322047.0182822020{'type': 'Point', 'coordinates': [-122.19975, 47.37483]}PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303303e+10
7JTDKN3DP9FKitsapBainbridge IslandWA98110.02015TOYOTAPRIUSPlug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range6023.0177904170{'type': 'Point', 'coordinates': [-122.521, 47.62732]}PUGET SOUND ENERGY INC5.303509e+10
81G1FY6S07LKitsapPort OrchardWA98367.02020CHEVROLETBOLT EVBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible259026.0132558002{'type': 'Point', 'coordinates': [-122.68471, 47.50524]}PUGET SOUND ENERGY INC5.303509e+10
91G1RD6S55KYakimaYakimaWA98908.02019CHEVROLETVOLTPlug-in Hybrid Electric Vehicle (PHEV)Clean Alternative Fuel Vehicle Eligible53014.0474853417{'type': 'Point', 'coordinates': [-120.60199, 46.59817]}PACIFICORP5.307700e+10
vin_1_10countycitystatezip_codemodel_yearmakemodelev_typecafv_typeelectric_rangebase_msrplegislative_districtdol_vehicle_idgeocoded_columnelectric_utility_2020_census_tract
99867SAYGDEE8RKingBellevueWA98006.02024TESLAMODEL YBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0041.0272236009{'type': 'Point', 'coordinates': [-122.12096, 47.55584]}PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303302e+10
99875YJ3E1EA3PKingMedinaWA98039.02023TESLAMODEL 3Battery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0048.0272958325{'type': 'Point', 'coordinates': [-122.23892, 47.61613]}PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303302e+10
9988WBY73AW08PKingSeattleWA98107.02023BMWI4Battery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0043.0245574395{'type': 'Point', 'coordinates': [-122.38591, 47.67597]}CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)5.303300e+10
9989YV4BR0CL5LKingBellevueWA98005.02020VOLVOXC90Plug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range18048.0321092600{'type': 'Point', 'coordinates': [-122.1621, 47.64441]}PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303302e+10
99907SAYGDED5RKingKentWA98031.02024TESLAMODEL YBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0047.0269115493{'type': 'Point', 'coordinates': [-122.17743, 47.41185]}PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303303e+10
99913FMTK4SE9MKingSammamishWA98074.02021FORDMUSTANG MACH-EBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0045.0276979587{'type': 'Point', 'coordinates': [-122.02054, 47.60326]}PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303303e+10
99921N4AZ0CP1EKingNewcastleWA98059.02014NISSANLEAFBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible84041.0130630342{'type': 'Point', 'coordinates': [-122.15716, 47.48852]}PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303303e+10
99937SAYGDEE9NKingRentonWA98056.02022TESLAMODEL YBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0011.0204703047{'type': 'Point', 'coordinates': [-122.1805, 47.50006]}PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303303e+10
99945YJ3E1EB6RClarkVancouverWA98662.02024TESLAMODEL 3Battery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0017.0269514556{'type': 'Point', 'coordinates': [-122.57722, 45.64251]}BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF CLARK COUNTY - (WA)5.301104e+10
99957SAYGDEF9NKingBellevueWA98008.02022TESLAMODEL YBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0048.0212097700{'type': 'Point', 'coordinates': [-122.11867, 47.63131]}PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303302e+10